So many little ways that hatred of some underclass — immigrants, the homeless, the young — requires making things worse for everyone so those in the under class don't get anything nice either. Whole smorgasbord of shooting yourself in the foot out of spite
have to keep ourselves poorer just to keep brown people out. can't have public bathrooms because the homeless might use them. have to play high-pitched noise to keep teens away. we are letting the cruelest people among us make our lives worse so they can be hateful
Putting to rest my joking feud with my Mexican friends about the world cup. We should be bonding over our shared commiseration: We are not partying the way we ought to be
I remember when we went to Mérida last year we shared shots of tequila with strangers and promised to stay in touch forever and we never did because we were too far gone to take normal pictures of each other's WhatsApp profiles. One of the most euphoric experiences of my life. That's how I feel watching the celebrations. That should be me, goddamn it
Reminds me of when my dad lived in LA his coworkers taught him some Spanish and they were like "pendejo is like a friendly way of calling someone dude or buddy" so he called one of their female coworkers "pendejo" and they were like "JOHN, PLEASE, that is a LADY, you call her pendeja"
These are the first 6 points of the 19 that Elizabeth Ginexi has listed in her summary of the proposed bill. I do recommend reading through all of them.
Right now it looks like the public comment period is set to close Monday, July 13, 2026.
Text of tweet under the cut because it is loooong.
But... Stochastic Parrots.
Timnit Gebru was fired from Google in December 2020 for refusing to retract a research paper, and every single warning that paper made about large language models has now happened at a scale the industry spent 4 years trying to make people forget about.
Her name is Timnit Gebru.
She co-led the Ethical AI team at Google. She co-wrote a paper called "On the Dangers of Stochastic Parrots" with Emily Bender at the University of Washington and two other researchers. The paper was 14 pages long. It was submitted to a top AI ethics conference. And it was the reason Google decided that one of the most senior Black women in AI research could no longer work there.
The story Google told publicly was that she resigned. The story she told, confirmed by 2,695 of her colleagues in an open letter, was that she was fired by email while on vacation because she refused to either retract the paper or remove her name from it.
The paper had not even been published yet.
Here is what she actually wrote, and why every prediction inside it has now come true.
The first warning was about scale itself. Bender and Gebru argued that training ever-larger models on ever-larger scrapes of the internet would produce systems that appeared fluent but had no actual understanding of language. They called these systems stochastic parrots because they would repeat patterns from training data with statistical confidence and zero comprehension. The paper predicted that this apparent intelligence would fool both users and developers into trusting outputs that were structurally incapable of being reliable.
This was 2020. GPT-3 had just come out. The paper predicted the hallucination problem before anyone had a word for it.
The second warning was about bias amplification. The paper documented in detail that internet-scale training data contains systematic overrepresentation of dominant viewpoints and underrepresentation of marginalized ones. The models would not just absorb this bias. They would amplify it, because the optimization process rewards confident outputs, and confidence in language patterns tracks frequency in the training set.
The prediction was that hiring tools built on these models would discriminate against women. That healthcare triage tools would underperform on Black patients. That loan approval systems would entrench inequality while presenting their decisions as neutral algorithmic judgment.
Every one of those things has now been documented in deployment.
Amazon's hiring algorithm penalized resumes that contained the word "women" in any context. Healthcare risk scoring algorithms used by major US hospitals were found to systematically underestimate the medical needs of Black patients. Apple Card's credit algorithm gave wives credit lines 10x lower than their husbands for the same financial profile.
The third warning was about environmental cost. The paper calculated that training a single large language model produced emissions equivalent to the lifetime output of 5 cars. The prediction was that the race to scale would create an environmental footprint that would eventually rival entire industries.
In 2024, Google's emissions were up 48% from 2019, and the company explicitly blamed AI infrastructure. Microsoft's were up 29%, same reason. Both companies have now quietly abandoned the climate commitments they were publicly celebrating the year Gebru was fired.
The fourth warning was about documentation. The paper argued that the training datasets being assembled were too large for anyone to actually audit. Nobody at Google, OpenAI, Meta, or any other lab could tell you with confidence what was in the data their models were trained on. This was not a temporary problem to be solved later. It was a permanent feature of the approach.
In 2023, researchers discovered that the LAION-5B dataset, used to train Stable Diffusion and other major image models, contained thousands of images of child sexual abuse material. The companies that had trained on the dataset had no way of knowing. The paper predicted that category of failure 3 years before it was found.
The fifth warning was the one Google cared about most.
Bender and Gebru argued that the deployment of these systems would centralize linguistic and cultural power in the hands of the small number of companies that could afford to train them. The internet would become a place where the dominant voice was a statistical average of dominant voices, presented as a neutral assistant. Languages underrepresented in the training data would degrade over time as more web content was generated by these systems and fed back into the next training run.
This is now happening in real time. A 2024 study found that 57% of new web content in English is AI-generated or AI-assisted. Researchers studying low-resource languages have documented active degradation in translation quality, because the synthetic content fed back into training is itself worse in those languages.
The paper Google fired her for predicted the model collapse problem before model collapse had a name.
The mechanism behind why this all happened is the part of her work that nobody quotes.
Gebru's argument was not that AI is dangerous in some abstract sci-fi sense. Her argument was that AI is dangerous in a very specific structural sense. The technology was being built by a small group of researchers who shared similar backgrounds, worked at similar companies, and were rewarded for shipping products faster than competitors. The incentive structure made it impossible for safety, ethics, and bias concerns to slow anything down. Anyone inside the system who raised those concerns was either ignored, sidelined, or removed.
She was making that argument from inside Google.
Then Google proved her right by removing her.
The team Google had built to make sure their AI was safe was dismantled in 90 days because they did the job they had been hired to do. Margaret Mitchell, the other co-lead of the Ethical AI team, was fired two months after Gebru for searching through her own emails for evidence of how Gebru had been treated.
Gebru did not stop. She founded DAIR, the Distributed AI Research Institute, in 2021. The mission is to do AI research outside the control of the companies that have a financial interest in not hearing the answers.
Every prediction in the Stochastic Parrots paper has now been validated by deployment. Hallucinations are an industry-wide problem the largest labs cannot solve. Bias amplification has been documented in hiring, healthcare, lending, and criminal justice. Environmental costs are larger than entire small countries. Training data audits remain impossible. Model collapse is an active research crisis at every major lab.
The question worth sitting with is the one almost no one in the industry will say out loud.
Every researcher with the technical credibility to call out these problems watched what happened to her in December 2020 and made a calculation about their own career. The number of people willing to speak publicly about safety and ethics issues inside the major AI labs collapsed after that firing and has not recovered.
The researcher Google fired for warning about exactly what is now happening was right.
The company that fired her is now the second-largest deployer of the technology she warned about.
And the people inside that company who agree with her are not allowed to say so.
“The LEGO Movie was my favorite movie of 2014, but it strikes me that the main character was male, because I feel like in our current culture, he HAD to be. The whole point of Emmett is that he’s the most boring average person in the world. It’s impossible to imagine a female character playing that role, because according to our pop culture, if she’s female she’s already SOMEthing, because she’s not male. The baseline is male. The average person is male. You can see this all over but it’s weirdly prevalent in children’s entertainment. Why are almost all of the muppets dudes, except for Miss Piggy, who’s a parody of femininity? Why do all of the Despicable Me minions, genderless blobs, have boy names? I love the story (which I read on Wikipedia) that when the director of The Brave Little Toaster cast a woman to play the toaster, one of the guys on the crew was so mad he stormed out of the room. Because he thought the toaster was a man. A TOASTER. The character is a toaster. I try to think about that when writing new characters— is there anything inherently gendered about what this character is doing? Or is it a toaster?”
— Bojack Horseman creator Raphael Bob-Waksberg commenting on how weird gendered defaults in entertainment are, and why we should think twice about them. Excerpted from this longer original post.
(via 360degreesasthecrowflies)
ohhhh shit. target is recalling their up & up baby wipes (fragrance free & fresh cucumber scented) because they're contaminated with Burkholderia cepacia complex and Burkholderia gladioli, multiple people are reporting discoloration & infections. i just got a call about it cuz i had purchased those but i've already gone through them 😅 so no refund for me. but im fine. if you have these they're saying you need to immediately stop using them and bring them back to target for a full refund. this bacteria can cause life threatening infections in children/infants and people with compromises immune systems (ESPECIALLY cystic fibrosis!!) and i know lots of other chronically ill people follow me!!!!
Despite the waste, the corruption, the displacement, and all else that comes with them right now, big international events like the World Cup and the Olympics are worth it. Koreans taking shots with Mexicans, the people of Kansas becoming close friends with the people of Algeria… this is what it’s all about. Despite it all, contact theory may still save us yet
You ever think about many peices of media have zero women and thats just perfectly normal but if a peice of media has an all female cast people get... like that? Women should be allowed to kill over this btw
